• CNN Apprehension Summary Report.
  
• Wall Pledge Reality.
  
• Apprehension Analysis.
  • 2000-2017 Apprehension Summary.
    
  • 2010 Vs 2017 Apprehension by Sector.
    
• Data Visualization
• Sector Statistical Plots.
  • Highest Apprehension per Sector

US-Mexico boarder apprehensions have been steadily declining since 2000, and in April of 2017 they reached historic lows. These downward trends and record lows can be accredited to President Trump’s election. The data shows that President Trump’s aggression towards immigration laws is having a deterring effect, and migrants are less enticed to enter the country.

DHS credits the administration policy as the lowest apprehensions prior to this past April were in December of 2011. This new low is 7,000 apprehensions less than the previous. In a year since April 2016, apprehensions were down 62%, though there are multiple different reasons for such a change. Throughout these slides you can find the monthly summaries of apprehensions by sector in a time series from 2000 to 2017 depicting the changes throughout time. But first, on the next slide will be the video linked to this report.

The following graph represents a Summary of the Apprehensions made at the US-Mexico border between the years 2000 through the year 2017. Each green marker represents the average apprehension rate for that year.

It is seen from the previous slide that Apprehensions throughout time have been slowly decreasing, and it is now as low as it once was back in the year 2011. This is being accredited to Donald Trumps aggression on border patrol and the desire to build a higher wall to prevent the US from having Mexicans or anybody fleeing their country to illegally cross the border into the states

Looking at the data and being able to manipulate and run a quick analysis on the apprehensions for both 2010 and 2017 we came up with the following conclusions for the most apprehensions in those years respectively by using the following R scrypt:

ts1 <- read.csv('PB monthly summaries.csv',row.names=1)
mat <- as.matrix(ts1)

#top 3 apprehensions per year in 2010 and 2017
max_apps_2010 <- tail(sort(mat["2010", ]), 3)
max_apps_2017 <- tail(sort(mat["2017", ]), 3)

Depicted in the table above, it is noted that the maximum apprehensions in the year 2010 were 61361 in the month of March, and the maximum apprehensions in the year 2017 were 47211 in the month of November.

Using simple statistical tests like those demonstrated in class we were able to compare the sector with the most apprehensions in 2010 with the sector with the most apprehensions in 2017. The difference in the maximum apprehensions for each year was a total of 14150. Noting that Apprehensions since the year 2010 were significantly lower in 2017.

Depicted from the plot above it can be seen that the most apprehensions for this sector happened in the month of October in the year 2017

Depicted from the plot above it can be seen that the most apprehensions for this sector happened in the month of October in the year 2017

Depicted from the plot above it can be seen that the most apprehensions for this sector happened in the month of March in the year 2010

Depicted from the plot above it can be seen that the most apprehensions for this sector happened in the month of November in the year 2017

Depicted from the plot above it can be seen that the most apprehensions for this sector happened in the month of April in the year 2010

Depicted from the plot above it can be seen that the most apprehensions for this sector happened in the month of November in the year 2017

Depicted from the plot above it can be seen that the most apprehensions for this sector happened in the month of March in the year 2010

Depicted from the plot above it can be seen that the most apprehensions for this sector happened in the month of March in the year 2010

Depicted from the plot above it can be seen that the most apprehensions for this sector happened in the month of October in the year 2017